24 citations · 71 across the 30 of their papers we have counts for
31 papers
LOInK: Learned Optimal Inverse Kinematics via Structured Neural Surrogate Models
Michael Somerfield, Damian Abood, Ruigang Wang +1
We introduce Learned Optimal Inverse Kinematics (LOInK), a method to generate approximately optimal solutions to an inverse kinematics problem. When trained on data consisting of s…
Robustly Invertible Nonlinear Dynamics and the BiLipREN: From Inversion-Based Control to Generative Trajectory Modelling
Yurui Zhang, Ruigang Wang, Ian R. Manchester
This paper proposes a new notion of robust invertibility for nonlinear dynamical systems, and introduces constructive parameterizations of recurrent neural network which are robust…
Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning
Dechuan Liu, Ruigang Wang, Ian R. Manchester
This paper presents a learning-based approach for all-pairs motion planning, where the initial and goal states are allowed to be arbitrary points in a safe set. We construct smooth…
Remarks on Lipschitz-Minimal Interpolation: Generalization Bounds and Neural Network Implementation
Arthur C. B. de Oliveira, Ruigang Wang, Ian R. Manchester +1
This note establishes a theoretical framework for finding (potentially overparameterized) approximations of a function on a compact set with a-priori bounds for the generalization…
React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN
Nicholas H. Barbara, Ruigang Wang, Alexandre Megretski +1
We study parameterizations of stabilizing nonlinear policies for learning-based control. We propose a structure based on a nonlinear version of the Youla-Kucera parameterization co…
Robustly Invertible Nonlinear Dynamics and the BiLipREN: Contracting Neural Models with Contracting Inverses
Yurui Zhang, Ruigang Wang, Ian R. Manchester
We study the invertibility of nonlinear dynamical systems from the perspective of contraction and incremental stability analysis and propose a new invertible recurrent neural model…
Negative Imaginary Neural ODEs: Learning to Control Mechanical Systems with Stability Guarantees
Kanghong Shi, Ruigang Wang, Ian R. Manchester
We propose a neural control method to provide guaranteed stabilization for mechanical systems using a novel negative imaginary neural ordinary differential equation (NINODE) contro…
R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks
Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester
This paper presents the Robust Recurrent Deep Network (R2DN), a scalable parameterization of stable and robust recurrent neural networks for machine learning and data-driven contro…
Norm-Bounded Low-Rank Adaptation
Ruigang Wang, Krishnamurthy Dvijotham, Ian R. Manchester
In this work, we propose norm-bounded low-rank adaptation (NB-LoRA) for parameter-efficient fine tuning. NB-LoRA is a novel parameterization of low-rank weight adaptations that adm…
LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
Patricia Pauli, Ruigang Wang, Ian Manchester +1
We propose a novel layer-wise parameterization for convolutional neural networks (CNNs) that includes built-in robustness guarantees by enforcing a prescribed Lipschitz bound. Each…
On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester
This paper presents a study of robust policy networks in deep reinforcement learning. We investigate the benefits of policy parameterizations that naturally satisfy constraints on…
Learning Stable and Passive Neural Differential Equations
Jing Cheng, Ruigang Wang, Ian R. Manchester
In this paper, we introduce a novel class of neural differential equation, which are intrinsically Lyapunov stable, exponentially stable or passive. We take a recently proposed Pol…
Monotone, Bi-Lipschitz, and Polyak-Lojasiewicz Networks
Ruigang Wang, Krishnamurthy Dvijotham, Ian R. Manchester
This paper presents a new bi-Lipschitz invertible neural network, the BiLipNet, which has the ability to smoothly control both its Lipschitzness (output sensitivity to input pertur…
RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness
Nicholas H. Barbara, Max Revay, Ruigang Wang +2
Neural networks are typically sensitive to small input perturbations, leading to unexpected or brittle behaviour. We present RobustNeuralNetworks.jl: a Julia package for neural net…
Learning Over Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems (Extended Version)
Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester
This paper presents a policy parameterization for learning-based control on nonlinear, partially-observed dynamical systems. The parameterization is based on a nonlinear version of…
Lipschitz-bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian
Patricia Pauli, Ruigang Wang, Ian R. Manchester +1
We establish a layer-wise parameterization for 1D convolutional neural networks (CNNs) with built-in end-to-end robustness guarantees. In doing so, we use the Lipschitz constant of…
Direct Parameterization of Lipschitz-Bounded Deep Networks
Ruigang Wang, Ian R. Manchester
This paper introduces a new parameterization of deep neural networks (both fully-connected and convolutional) with guaranteed Lipschitz bounds, i.e. limited sensitivity to…
Model Predictive Control of Spreading Processes via Sparse Resource Allocation
Ruigang Wang, Armaghan Zafar, Ian R. Manchester
In this paper, we propose a model predictive control (MPC) method for real-time intervention of spreading processes, such as epidemics and wildfire, over large-scale networks. The…
Youla-REN: Learning Nonlinear Feedback Policies with Robust Stability Guarantees
Ruigang Wang, Ian R. Manchester
This paper presents a parameterization of nonlinear controllers for uncertain systems building on a recently developed neural network architecture, called the recurrent equilibrium…
Learning over All Stabilizing Nonlinear Controllers for a Partially-Observed Linear System
Ruigang Wang, Nicholas H. Barbara, Max Revay +1
This paper proposes a nonlinear policy architecture for control of partially-observed linear dynamical systems providing built-in closed-loop stability guarantees. The policy is ba…
Contraction-Based Methods for Stable Identification and Robust Machine Learning: a Tutorial
Ian R. Manchester, Max Revay, Ruigang Wang
This tutorial paper provides an introduction to recently developed tools for machine learning, especially learning dynamical systems (system identification), with stability and rob…
Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics
Ruigang Wang, Patrick J. W. Koelwijn, Ian R. Manchester +1
In this paper, we present a virtual control contraction metric (VCCM) based nonlinear parameter-varying (NPV) approach to design a state-feedback controller for a control moment gy…
Recurrent Equilibrium Networks: Flexible Dynamic Models with Guaranteed Stability and Robustness
Max Revay, Ruigang Wang, Ian R. Manchester
This paper introduces recurrent equilibrium networks (RENs), a new class of nonlinear dynamical models} for applications in machine learning, system identification and control. The…
Reduced-Order Nonlinear Observers via Contraction Analysis and Convex Optimization
Bowen Yi, Ruigang Wang, Ian R. Manchester
In this paper, we propose a new approach to design globally convergent reduced-order observers for nonlinear control systems via contraction analysis and convex optimization. Despi…